Giorgio Grisetti

Ricercatore


grisetti@diag.uniroma1.it
Stanza: B115
Tel: +39 0677274121
Fax: +39 0677274106
Biografia: 

Giorgio Grisetti is assistant professor at Sapienza University of Rome. He is member of the RoCoCo lab at La Sapienza since November 2010. He is also member of the Autonomous Intelligent Systems Lab. at Freiburg University headed by Wolfram Burgard where he worked as a Post Doc since 2006. His research interests lie in the areas of mobile robotics. His previous and current works aimes to provide effective solutions to mobile robot navigation in all its aspects: SLAM, localization and path planning. He was a PhD student at University of Rome "La Sapienza" in the Intelligent Systems Lab. His advisor was Daniele Nardi and he received his PhD degree in April 2006. His PhD thesis focused on SLAM using Rao-Blackwellized particle filters. In 2001, he received his M.Sc. degree in computer engineering, at the University of Rome.Current Teaching Activities

Supervised Theses

  • Michael Ruhnke, 2007, Unsupervised Learning of 3D Object Models from Partial Views. Diplomarbeit, University of Freiburg. Published in proc. of ICRA 07. paper.
  • Bastian Steder, 2007, Learning Maps in 3D using Attitude and Noisy Vision Sensors Diplomarbeit, University of Freiburg. Published in proc. of IROS 07. paper.
  • Slawomir Grzonka, 2007, Look-ahead Proposals for Robust Grid-Based SLAM , University of Freiburg, Diplomarbeit. Published in Proc. of FSR-07. paper.
  • Gian Diego Tipaldi, 2007, Speeding Up Rao-Blackwellized SLAM, ``La Sapienza'', Master's thesis. Published in Proc. of ICRA 06. Published in Journal of autonomous robots 06. paper;
  • Andrea Censi, 2005, Scan Matching in the Hough Domain , ``La Sapienza'', Bachelor's thesis. Published in in Proc. of Int.~Conf.~on Robotics and Automation (ICRA), 2005. paper
  • Luca Marchetti, 2005 A Comparative Analysis of Particle Filter based Localization Methods `La Sapienza'', Master's thesis. Published in Proc. of RoboCup Symposium, 2006.
  • Sergio Lo Cascio, 2003, Design and Evaluation of Multi Agent Systems for Rescue Operations , `` La Sapienza'',Master's thesis. Published in Proc. of IROS 03.
  • Past Teaching Activities
  • During my activity, I was assistant of the courses of ''Fondamenti di Informatica 1'' (Basics of computer science) and Artificial Intelligence. For AI, I was also co-author with prof. Daniele Nardi of a set of notes on functional and logic languages. Additionally, I gave several seminars on state estimation, in the courses of AI and Vision and Perception. During and after the PhD, I advised several master thesis whose contents were often published in international conferences or workshops. This is a summary of those times:

    Pubblicazioni: 
    • FARINELLI; G. GRISETTI; IOCCHI L; LO CASCIO S; NARDI D (2002) Coordination in dynamic environments with constraints on resources. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems (IROS)  
    • FARINELLI A.; GRISETTI G.; L. IOCCHI; NARDI D.; ROSATI R. (2002) Generation and Execution of Partially Correct Plans in Dynamic Environments. Proc. of 3rd International Cognitive Robotics Workshop (COGROB)  
    • F. D'AGOSTINO; A. FARINELLI; G. GRISETTI; L.  IOCCHI; D. NARDI (2002) Monitoring and Information Fusion for Search and Rescue Operations in Large-Scale Disasters. 5th International Conference on Information Fusion 672 679  
    • G. GRISETTI; L. IOCCHI; D. NARDI (2002) Global Hough Localization for Mobile Robots in Polygonal Environments. International Conference on Robotics and Automation (ICRA) 353 358  
    • FARINELLI A; GRISETTI G; IOCCHI L; D. NARDI (2003). Cognitive Soccer Robots ERCIM NEWS (ISSN:0926-4981), 26- 27, 53;  
    • Farinelli A., Grisetti G., Iocchi L., Lo Cascio S., Nardi  D. (2003) Design and evaluation of multi agent systems for rescue operations . Proceedings of Intelligent Robots and Systems, 2003. (IROS 2003) 3138 3143  
    • A. FARINELLI; G. GRISETTI; L. IOCCHI (2004) SPQR-RDK: a modular framework for programming mobile robots.. RoboCup 2004: Robot Soccer World Cup VIII 653 660 2004 Lisbon,  
    • STACHNISS C; G. GRISETTI; HAEHNEL D; BURGARD W (2004) Improved rao-blackwellized mapping  by adaptive sampling and active loop-closure. Proc. of the Workshop on Self-Organization of AdaptiVE behavior (SOAVE)  
    • BAHADORI S; CESTA A; GRISETTI G; IOCCHI L; LEONE R; D. NARDI; ODDI A; PECORA F; RASCONI R (2004). RoboCare: Pervasive Intelligencefor the Domestic Care of the Elderly INTELLIGENZA ARTIFICIALE (ISSN:1724-8035), 16- 21, 1(1);  
    • BAHADORI S; CALISI D; CENSI A; FARINELLI A; G. GRISETTI; IOCCHI L; NARDI D (2004) Intelligent systems for search and rescue. Proc. of IROS Workshop ”Urban search and rescue: from Robocup to real world applications”  
    • FARINELLI A; GRISETTI G; IOCCHI L; LO CASCIO S; D. NARDI (2004) RoboCup Rescue Simulation: Methodologies, Tools and Evaluation for Practical Applications. RoboCup 2003: Robot Soccer World Cup VII Springer Verlag HEIDELBERG 645 652 LNAI 3020 July 2003 Padova, Italy,  
    • W. BURGARD; C. STACHNISS; G. GRISETTI (2005) Information gain-based exploration using Rao-Blackwellized particle filters. Proc. of the Learning Workshop (Snowbird)  
    • STACHNISS C; HAEHNEL D; BURGARD W; G. GRISETTI (2005). On actively closing loops in grid-based  fastslam ADVANCED ROBOTICS (ISSN:0169-1864),  
    • G. GRISETTI; STACHNISS C; BURGARD W (2005) Improving Grid-Based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling. Proc. of the IEEE Int. Conf. onRobotics & Automation (ICRA) 2432 2437  
    • A. CENSI; L. IOCCHI; G. GRISETTI (2005) Scan Matching in the Hough Domain. Proc. of International Conference on Robotics and Automation (ICRA) 2739 2744 18-22/4/2005 Barcelona, Spain,  
    • S. BAHADORI; G. GRISETTI; L. IOCCHI; G.R. LEONE; D. NARDI (2005) Real-Time Tracking of Multiple People through Stereo Vision. IEE International Workshop on Intelligent Environments 252 259 28-29/6/2005 Colchester, UK,  
    • STACHNISS C; G. GRISETTI; BURGARD W (2005) Information-Gain Based Exploration Using Rao-Blackwellized Particle Filters. Proc. of Robotics: Science and Systems (RSS) MIT Press, Cambridge, Massachusetts, 2008 Edited by Wolfram Burgard, Oliver Brock and Cyrill Stachniss 65 72  
    • STACHNISS C; G. GRISETTI; BURGARD W (2005) Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM after Actively Closing Loops. Proc. of the IEEE Int. Conf. on Robotics &Automation (ICRA) 655 660  
    • P. LAMON; C. STACHNISS; R. TRIEBEL; PFAFF P; C. PLAGEMANN; G. GRISETTI; S. KOLSKI; W. BURGARD; R. SIEGWART (2006) Mapping with an autonomous car. Proc. of the IEEE Int. Workshop on IntelligentRobots and Systems & (IROS) 3485 3490  
    • G. GRISETTI; TIPALDI G.D; STACHNISS C; BURGARD W; NARDI D (2006) Speeding Up Rao Blackwellized SLAM.. Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) IEEE 442 447 Maggio 2006 Orlando, USA,  
    • A. FARINELLI; G. GRISETTI; L. IOCCHI (2006). Design and implementation of modular software for programming mobile robots INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS (ISSN:1729-8806), 37- 42, 3 (1);  
    • B. STEDER; G. GRISETTI; S. GRZONKA; C. STACHNISS; A. ROTTMANN; W. BURGARD (2007) Learning maps in 3D using attitude and noisy vision sensors. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems (IROS) 644 649  
    • G. GRISETTI; STACHNISS C; BURGARD W (2007). Improved techniques for grid mapping with rao-blackwellized particle filters IEEE TRANSACTIONS ON ROBOTICS (ISSN:1552-3098), 34- 46, 23;  
    • STACHNISS. S; G. GRISETTI; MARTINEZ MOZOS O; BURGARD W (2007). Efficiently learning metric and  topological maps with autonomous service robots INFORMATION TECHNOLOGY (ISSN:null), 232- 237, 49;  
    • G. GRISETTI; C. STACHNISS; S. GRZONKA; W. BURGARD (2007) A tree parameterization for efficiently computing maximum likelihood maps using gradient descent. Proc. of Robotics: Science and Sys-tems (RSS)  
    • G. GRISETTI; TIPALDI G.D; STACHNISS C; BURGARD W; NARDI D (2007). Fast and Accurate SLAM with Rao-Blackwellized Particle Filters ROBOTICS AND AUTONOMOUS SYSTEMS (ISSN:0921-8890), 30- 38, 55 (1);  
    • D. CALISI; A. FARINELLI; G. GRISETTI; L. IOCCHI; D. NARDI; S. PELLEGRINI; D. TIPALDI; V. A. ZIPARO (2007) Uses of Contextual Knowledge in Mobile Robots. 10th Congress of the AI*IA 543 554 2007 Roma,  
    • K.M. WURM; C. STACHNISS; G. GRISETTI; W. BURGARD (2007) Improved Simultaneous Localization and  Mapping Using a Dual Representation of the Environment. Proc. of the European Conference onMobile Robots (ECMR)  
    • S. GRZONKA; C. PLAGEMANN; G. GRISETTI; W. BURGARD (2007) Look-ahead proposals for robust grid-based SLAM. Proc. of Field and Service Robotics Springer 329 338  
    • G. GRISETTI; GRZONKA S; STACHNISS C; PFAFF P; BURGARD W (2007) Efficient Estimation of Accurate  Maximum Likelihood Maps in 3D. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems(IROS) 3472 3478  
    • L. MARCHETTI; G. GRISETTI; L. IOCCHI (2007) A Comparative Analysis of Particle Filter based Localization Methods. RoboCup 2006: Robot Soccer World Cup X Springer BERLIN / HEIDELBERG 442 449 LNCS 4434/2007 2006 Bremen, Germany,  
    • G.D. TIPALDI; G. GRISETTI; W. BURGARD (2007) Approximate covariance estimation in graphical approaches to SLAM. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems (IROS) 3460 3465  
    • C. STACHNISS; G. GRISETTI; W. BURGARD (2007) Analyzing Gaussian proposal distributions for mapping with rao-Blackwellized particle filters. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems(IROS) 3485 3490  
    • GRZONKA S; BOUABDALLAH S; G. GRISETTI; BURGARD W; SIEGWART R (2008) Towards a fully autonomous indoor helicopter. Proc of. IROS Workshop on Visual guidance systems for small autonomous aerial vehicles  
    • S. GRZONKA ; G. GRISETTI; W. BURGARD (2008) Autonomous indoor navigation using a small size  quad-rotor. Proc. of International Workshop on Simulation, Modeling, and Programming forAutonomous Robots (SIMPAR) 455 463  
    • STACHNISS C; BENNEWITZ M; G. GRISETTI; BEHNKE S; BURGARD W (2008) How to Learn Accurate Grid Maps with a Humanoid. Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) 3194  3199  
    • G. GRISETTI; LODI RIZZINI D; STACHNISS C; OLSON E; BURGARD W (2008) Online Constraint Network Optimization for Efficient Maximum Likelihood Map-Learning. In Proc. of the IEEEInt. Conf. on Robotics & Automation (ICRA) 1880 1885  
    • STEDER B; G. GRISETTI; STACHNISS C; BURGARD W (2008). Visual SLAM for flying vehicles. IEEE TRANSACTIONS ON ROBOTICS (ISSN:1552-3098),  
    • KUEMMERLE R; STEDER B; DORNHEGE C; KLEINER A; G. GRISETTI; BURGARD W (2009) Large scale graph-based SLAM using aerial images as prior information. Proc. of the IEEE Int. Conf. of Robots, Science and Systems (RSS)  
    • WURM K; STACHNISS C; G. GRISETTI (2009). Bridging the Gap Between Feature and Grid-Based SLAM ROBOTICS AND AUTONOMOUS SYSTEMS (ISSN:0921-8890), 140- 148, 58(2);  
    • GRZONKA S; PLAGEMANN C; G. GRISETTI; BURGARD W (2009). Look-ahead Proposals for Robust Grid-Based SLAM with Rao-Blackwellized Particle Filters INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (ISSN:0278-3649), 329- 338, 42;  
    • G. GRISETTI; STACHNISS C; BURGARD W (2009). Nonlinear constraint network optimization for efficient map learning IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (ISSN:1524-9050), 428- 439, 10(3);  
    • BURGARD W; STACHNISS C; G. GRISETTI; STEDER B; KUEMMERLE R; DORNHEGE C; RUHNKE M; KLEINER A; TARDOS J.D (2009) Comparison of SLAM Algorithms Based on a Graph of Relations. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems (IROS), 2089 2095  
    • GRZONKA S; G. GRISETTI; BURGARD W (2009) Towards a Navigation System for Autonomous Indoor Flying. Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) 2878 2883  
    • STEDER B; G. GRISETTI; VAN LOOCK M; BURGARD W (2009) Robust on-line model-based object  detection from range images. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems(IROS) 4739 4744  
    • RUHNKE M; STEDER B; G. GRISETTI; BURGARD W (2009) Unsupervised learning of 3D object models  from partial views. Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) 801 806   
    • KUEMMERLE R; STEDER B; DORNHEGE C; KLEINER A; G. GRISETTI; BURGARD W (2010). Large Scale Graph-based SLAM using Aerial Images as Prior Information. AUTONOMOUS ROBOTS (ISSN:0929-5593), 25- 39, 30(1);  
    • M. RUHNKE; B. STEDER; G. GRISETTI; W. BURGARD (2010) Unsupervised learning of compact 3D models  based on the detection of recurrent structures. Proc. of the IEEE Int. Conf. on Intelligent Robots & Systems (IROS) 22 29  
    • G. GRISETTI; R.  KUEMMERLE; C. STACHNISS; J. HERTZBERG ; U. FRESE (2010) Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping.. Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA) 273 278  
    • STEDER B; G. GRISETTI; BURGARD W (2010) Robust place recognition for 3D range data based on point features. Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA) 1400 1405  
    • Samir  Bouabdallah,Christian  Bermes,Slawomir  Grzonka,Christiane  Gimkiewicz,Alain  Brenzikofer,Robert  Hahn,Dario  Schafroth,Giorgio  Grisetti,Wolfram  Burgard,Roland  Siegwart (2010). Towards Palm-Size Autonomous Helicopters JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS (ISSN:0921-0296), 445- 471, 61;  
    • K. KONOLIGE; G. GRISETTI; R. KUEMMERLE; W. BURGARD; B. LIMKETKAI; R. VINCENT (2010) Efficient sparse pose adjustment for 2D mapping. Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots & Systems(IROS) 22 29  
    • G. GRISETTI; KUEMMERLE R; STACHNISS C; BURGARD W (2010). A Tutorial on Graph-Based SLAM IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (ISSN:1939-1390), 31- 43, 2;  
    • Michael  Ruhnke; Rainer  Kummerle; Giorgio  Grisetti; Wolfram  Burgard (2011) Highly accurate maximum likelihood laser mapping by jointly optimizing laser points and robot poses2011 IEEE International Conference on Robotics and Automation. 2011 IEEE International Conference on Robotics and Automation 2812 2817  
    • R. Kümmerle, G. Grisetti, and W. Burgard.  (2011) Simultaneous Calibration, Localization, and Mapping. . In Proc. of the IEEEInt. Conf. on Robotics & Automation (ICRA)  
    • Henrik  Kretzschmar,Cyrill  Stachniss,Giorgio  Grisetti (2011) Efficient information-theoretic graph pruning for graph-based SLAM with laser range finders (2011)  IEEE/RSJ International Conference on Intelligent Robots and Systems 865 871  
    • R. Kuemmerle,  B. Steder,    C.Dornhege,   A. Kleiner,  G. Grisetti,  W. Burgard, (2011). Large Scale Graph-based {SLAM} using Aerial Images as Prior Information AUTONOMOUS ROBOTS (ISSN:0929-5593), 25- 39, 30;  
    • Rainer  Kummerle,Giorgio  Grisetti,Wolfram  Burgard (2011) Simultaneous calibration, localization, and mapping2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems 3716 3721  
    • Jakob  Ziegler,Henrik  Kretzschmar,Cyrill  Stachniss,Giorgio  Grisetti,Wolfram  Burgard (2011) Accurate human motion capture in large areas by combining IMU- and laser-based people tracking2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems 86 91  
    • R. Kümmerle, G. Grisetti, H. Strasdat, K. Konolige,  W. Burgard.  (2011) g2o: A General Framework for Graph Optimization. . Proc. of the IEEE Int. Conf. on Robotics &Automation (ICRA)  
    • M. Ruhnke, R. Kuemmerle, G. Grisetti, and W. Burgard.  (2011) Highly Accurate Maximum Likelihood Laser Mapping by Jointly Optimizing Laser Points and Robot Poses. Proc. of the IEEE Int. Conf. onRobotics & Automation (ICRA)  
    • Ruhnke, M., Kuemmerle, R., Grisetti, G., and Burgard, W.. (2011) Range Sensor Based Model Construction by Sparse Surface Adjustment. .  
    • Michael  Ruhnke,Bastian  Steder,Giorgio  Grisetti,Wolfram  Burgard (2012). 3D Environment Modeling Based on Surface PrimitivesTowards Service Robots for Everyday Environments. Springer Tracts in Advanced RoboticsTowards Service Robots for Everyday Environments. 281- 300, 76,  
    • R. Kümmerle, G. Grisetti,  W. Burgard.  (2012). Simultaneous Parameter Calibration, Localization, and Mapping ADVANCED ROBOTICS (ISSN:0169-1864), 2021- 2041, 26;  
    • G. Grisetti, R. Kümmerle, and K. Ni.  (2012) Robust Optimization of Factor Graphs by using Condensed Measurements. Proc. of the {IEEE/RSJ} Int. Conf. on Intelligent Robots and Systems (IROS IEE Conference Publication 581 588 Ottobre 2012 Vilamoura, Portugal,  
    • Rainer Kümmerle, Giorgio Grisetti, Wolfram Burgard (2012). Simultaneous Parameter Calibration, Localization, and Mapping. Advanced Robotics.     Taylor & Francis Limited:Rankine Road, Basingstoke RG24 8PR United Kingdom, 2021- 2041, 26 issue 17,  
    • Slawomir  Grzonka, Giorgio Grisetti,Wolfram  Burgard (2012). A Fully Autonomous Indoor Quadrotor IEEE TRANSACTIONS ON ROBOTICS (ISSN:1552-3098), 90- 100, 28;  
    • Y Asahara, T Yamamoto, M Van Loock, B Steder, G Grisetti, W Burgard (2012). OBJECT RECOGNITION METHOD, OBJECT RECOGNITION APPARATUS, AND AUTONOMOUS MOBILE ROBOT. 
    Riconoscimenti e premi: 
    • October 2010 Nomination for the best IROS paper award. M. Ruhnke, B.Steder, G. Grisetti, W. Burgard, Unsupervised Learning of Compact 3D Models Based on the Detection of Recurrent Structures.
    • August 2010 Open Source achievement award from Willow Garage.
    • April 2010 Best paper award at the International Conference and Exhibition on Unmanned Areal Vehicles. Samir Bouabdallah, Christian Bermes, Slawomir Grzonka, Christiane Gimkiewicz, Alain Brenzikofer, Robert Hahn, Dario Schafroth, Giorgio Grisetti, Wolfram Burgard, Roland Siegwart. Towards Palm-Size Autonomous Helicopters.
    • April 2009 Best Paper award at ICRA 2009. Slawomir Grzonka, Giorgio Grisetti, Wolfram Burgard. Towards a Navigation System for Autonomous Indoor Flying.
    Area di ricerca: 
    Intelligenza artificiale e rappresentazione della conoscenza
    Interessi di ricerca: 

    Research StatementIn the future, robots and autonomous devices will become more and morepopular and will finally be part of our everyday life. To achieve thisgoal, robot need to act intelligently and to offer useful services totheir users. Technically, the goal of an intelligent robot is to bringthe environment to a desired configuration by interacting with it.  Toreach this goal the robot might perform complex sequences of actions.To calculate which actions to perform one needs to represent theenvironment at the level of abstraction appropriate for the specifictask.  Our research focuses to develop techniques and models that canbe used in relevant classes of robotic applications, namely:autonomous navigation, object detection and object recognition.  Themethods we developed have been used as building blocks of integratedrobotic systems which can explore the environment or that are used inindustrial applications.  To speed-up the development of robotapplications we also contributed to middle-wares for robotics and weinvestigated how to compare different systems.Learning Models for Autonomous NavigationAcquiring maps with mobile robots has been deeply investigated in thelast two decades.  This problem is known with the name of``Simultaneous Localization And Mapping'' (SLAM).  It consists inestimating simultaneously the map and the position of a mobile robotwhile it moves and measures the environment with its on-board sensors.Within this context, we contributed with approaches that are able toconstruct metric models of the environment by using either filteringor smoothing approaches on graph representations.SLAM with Particle FiltersDuring the first period of our research we investigated particlefilters for SLAM. These filters represent the probability distributionover the possible states of the tracked system via a set of samples.In SLAM, every sample represents a potential robot trajectory.  Weinvestigated how to accurately evolve this distribution of samples inorder to reduce their number, by placing them in the ``right''locations  An opensource implementation of these algorithms (GMapping ) became well known to therobotic community.To improve the efficiency of these algorithms, we subsequentlyinvestigated on more compact representations that can exploit thesymmetries in the environment and in the SLAMprocess.  These systems have beessuccessfully used in search and rescueapplications onhumanoids. They also have been extended tofixed-lag filters or to operate onmixed representations.Graph-based SLAMBased on the experience with particle filter SLAM, second phase of ourresearch we developed graph-based SLAM algorithms.  As the namesuggests, these approaches model the problem as a graph. Each node ofthe graph represents a local map, and the position of this local mapwithin a global reference system. An edge between two nodes representsa spatial constraint between two local maps and is labeled with thespatial transformation obtained by aligning the two local maps.  Thus,solving graph-based SLAM means:

    • constructing the graph and the labels from the raw sensor data (graph construction)  and
    • find the locations of the local maps that better satisfies the  constraints encoded in the edges (graph optimization).

    To construct the graph we developed efficient algorithms to alignlocal maps made of 2D laser scans, on images andinertial data or on 3Dscans. To reject wrong alignments we developedalgorithms to efficiently compute the relative uncertainty between twonodes.  To optimize the graph we developed an algorithm based onstochastic gradient descent.This algorithm relies on a tree parametrization of the graph to speedup the convergence.  An open-source implementation has been madeavailable TORO ) and it isactually used by many research groups, for instance Willow Garage,Carnegie Mellon University, University of T'uebingen, University of Orebro,University of Parma and others.When the robot moves continuously in the environment the full SLAMproblem in its naive formulation becomes not feasible due to the largeamount of data that needs to be processed at each cycle. To this endwe investigated the possibility of dropping the less informativenodes. Furthermore, we approached theoptimization of these large scale problems by using hierarchicalrepresentations HOGMAN). Object Detection and Object ModelingTo perform complex tasks in unstructured environments, a robot shouldbe able to identify the known objects that it needs to manipulate.  Inparticular, to manipulate an object a robot needs to find theinstances and the locations of this object in a scene starting from aknown model.  In a recent work, we approached the problem of detectingknown objects in a 3D scene.  The algorithmrelies on a compact representation of the three dimensional data(range images), and utilizes a variant of RANSAC to align a knownmodel on a portion of the scene. Subsequently, we developed analgorithm that learns models from the scenes in an unsupervisedway. This is done by aligning a portion of the scene onto another.The recurrent consistent portions are combined to form models.  Thisproblem has substantial overlap with multi-robot SLAMAutonomous ExplorationMoving autonomously is an essential skill for any application thatinvolves mobile robots. To this end one needs a map of theenvironment.  Thus, having a device which is able to autonomouslygather such an information is seen as highly relevant by the roboticcommunity. This task is called autonomous exploration.Traditional SLAM algorithms are substantially passive, in the sensethat they operate on a stream of data without controlling how thesedata are taken.  However, it is known that the particular trajectoryfollowed by the robot has a substantial effect on the outcome of SLAM.We introduced an entropy based measure to drive the robot.  This systemtakes into account the effects that a particular observation has onthe outcome of SLAM and chooses the one which maximizes theexpected information gain  about the environment.Integration and Comparison of SubsystemsAn important aspect of our research is the systematic comparison andthe evaluation of different approaches in different scenarios.We proposed a metric to compare SLAMalgorithms which relates the quality of the map to the energy requiredto ``deform" it to obtain the ground truth.  To quickly builddifferent applications involving mobile robots, we contributed to opensource systems with the realization of different components.  Wefurthermore contributed to the design and implementation of anavigation system for small-size helicopters available at (http://www.openquadrotor.org).